A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.
This report has been generated by the bigbio/quantms analysis pipeline. For information about how to interpret these results, please see the documentation.
Report
generated on 2025-07-01, 14:33 UTC
based on data in:
/home/runner/work/pmultiqc/pmultiqc/data
pmultiqc
pmultiqc is a MultiQC module to show the pipeline performance of mass spectrometry based quantification pipelines such as nf-core/quantms, MaxQuant.URL: https://github.com/bigbio/pmultiqc
Experiment Setup
Experimental Design
This table shows the design of the experiment. I.e., which files and channels correspond to which sample/condition/fraction.
You can see details about it in
https://abibuilder.informatik.uni-tuebingen.de/archive/openms/Documentation/release/latest/html/classOpenMS_1_1ExperimentalDesign.html
Showing 11/11 rows and 8/8 columns.
| Sample Name | MSstats Condition: SP | MSstats Condition: CT | MSstats Condition: QY | MSstats Condition: CV | MSstats BioReplicate | Fraction Group | Fraction | Label |
|---|---|---|---|---|---|---|---|---|
| 1 | Saccharomyces cerevisiae | Mixture | 10% | in-house | 1 | |||
| 1 | 1 | 1 | ||||||
| 2 | Saccharomyces cerevisiae | Mixture | 10% | in-house | 2 | |||
| 2 | 1 | 1 | ||||||
| 3 | Saccharomyces cerevisiae | Mixture | 10% | in-house | 3 | |||
| 3 | 1 | 1 | ||||||
| 4 | Saccharomyces cerevisiae | Mixture | 5% | in-house | 4 | |||
| 4 | 1 | 1 | ||||||
| 5 | Saccharomyces cerevisiae | Mixture | 5% | in-house | 5 | |||
| 5 | 1 | 1 | ||||||
| 6 | Saccharomyces cerevisiae | Mixture | 5% | in-house | 6 | |||
| 6 | 1 | 1 | ||||||
| 7 | Saccharomyces cerevisiae | Mixture | 5% | in-house | 7 | |||
| 7 | 1 | 1 | ||||||
| 8 | Saccharomyces cerevisiae | Mixture | 3.3% | in-house | 8 | |||
| 8 | 1 | 1 | ||||||
| 9 | Saccharomyces cerevisiae | Mixture | 3.3% | in-house | 9 | |||
| 9 | 1 | 1 | ||||||
| 10 | Saccharomyces cerevisiae | Mixture | 3.3% | in-house | 10 | |||
| 10 | 1 | 1 | ||||||
| 11 | Saccharomyces cerevisiae | Mixture | 3.3% | in-house | 11 | |||
| 11 | 1 | 1 |
Summary and HeatMap
Summary Table
This table shows the quantms pipeline summary statistics.
This table shows the quantms pipeline summary statistics.
Showing 1/1 rows and 5/5 columns.
| #MS2 Spectra | #Identified MS2 Spectra | %Identified MS2 Spectra | #Peptides Identified | #Proteins Identified | #Proteins Quantified |
|---|---|---|---|---|---|
| 806748 | 537848 | 66.67% | 61110 | 9346 | 8458 |
HeatMap
This heatmap provides an overview of the performance of the quantms.
This plot shows the pipeline performance overview. Some metrics are calculated.
* Heatmap score[Contaminants]: as fraction of summed intensity with 0 = sample full of contaminants;
1 = no contaminants
* Heatmap score[Pep Intensity (>23.0)]: Linear scale of the median intensity reaching the threshold,
i.e. reaching 2^21 of 2^23 gives score 0.25.
* Heatmap score[Charge]: Deviation of the charge 2 proportion from a representative
Raw file (median). For typtic digests, peptides of charge 2 (one N-terminal and one at
tryptic C-terminal R or K residue) should be dominant. Ionization issues (voltage?),
in-source fragmentation, missed cleavages and buffer irregularities can cause a shift
(see Bittremieux 2017, DOI: 10.1002/mas.21544).
* Heatmap score [Missed Cleavages]: the fraction (0% - 100%) of fully cleaved peptides per Raw file
* Heatmap score [Missed Cleavages Var]: each Raw file is scored for its deviation from the ‘average’ digestion
state of the current study.
* Heatmap score [ID rate over RT]: Judge column occupancy over retention time.
Ideally, the LC gradient is chosen such that the number of identifications
(here, after FDR filtering) is uniform over time, to ensure consistent instrument duty cycles.
Sharp peaks and uneven distribution of identifications over time indicate potential for LC gradient
optimization.Scored using ‘Uniform’ scoring function. i.e. constant receives good score, extreme shapes are bad.
* Heatmap score [MS2 Oversampling]: The percentage of non-oversampled 3D-peaks. An oversampled
3D-peak is defined as a peak whose peptide ion (same sequence and same charge state) was
identified by at least two distinct MS2 spectra in the same Raw file. For high complexity samples,
oversampling of individual 3D-peaks automatically leads to undersampling or even omission of other 3D-peaks,
reducing the number of identified peptides.
* Heatmap score [Pep Missing Values]: Linear scale of the fraction of missing peptides.
Created with MultiQC
Pipeline Result Statistics
This plot shows the quantms pipeline final result.
Including Sample Name, Possible Study Variables, identified the number of peptide in the pipeline,
and identified the number of modified peptide in the pipeline, eg. All data in this table are obtained
from the out_msstats file. You can also remove the decoy with the `remove_decoy` parameter.
Showing 11/11 rows and 9/9 columns.
| Sample Name | MSstats Condition: SP | MSstats Condition: CT | MSstats Condition: QY | MSstats Condition: CV | Fraction | #Peptide IDs | #Unambiguous Peptide IDs | #Modified Peptide IDs | #Protein (group) IDs |
|---|---|---|---|---|---|---|---|---|---|
| 1 | Saccharomyces cerevisiae | Mixture | 10% | in-house | |||||
| 1 | 35347 | 32810 | 4730 | 7417 | |||||
| 2 | Saccharomyces cerevisiae | Mixture | 10% | in-house | |||||
| 1 | 37165 | 34620 | 2533 | 7859 | |||||
| 3 | Saccharomyces cerevisiae | Mixture | 10% | in-house | |||||
| 1 | 37815 | 35263 | 1912 | 7959 | |||||
| 4 | Saccharomyces cerevisiae | Mixture | 5% | in-house | |||||
| 1 | 37130 | 34616 | 2013 | 7757 | |||||
| 5 | Saccharomyces cerevisiae | Mixture | 5% | in-house | |||||
| 1 | 37172 | 34700 | 1772 | 7710 | |||||
| 6 | Saccharomyces cerevisiae | Mixture | 5% | in-house | |||||
| 1 | 37289 | 34787 | 1781 | 7696 | |||||
| 7 | Saccharomyces cerevisiae | Mixture | 5% | in-house | |||||
| 1 | 37053 | 34559 | 1740 | 7726 | |||||
| 8 | Saccharomyces cerevisiae | Mixture | 3.3% | in-house | |||||
| 1 | 36879 | 34406 | 1946 | 7611 | |||||
| 9 | Saccharomyces cerevisiae | Mixture | 3.3% | in-house | |||||
| 1 | 37300 | 34850 | 1927 | 7632 | |||||
| 10 | Saccharomyces cerevisiae | Mixture | 3.3% | in-house | |||||
| 1 | 37239 | 34751 | 1735 | 7633 | |||||
| 11 | Saccharomyces cerevisiae | Mixture | 3.3% | in-house | |||||
| 1 | 36919 | 34486 | 1697 | 7637 |
Identification Summary
Number of Peptides identified Per Protein
This plot shows the number of peptides per protein in quantms pipeline final result
This statistic is extracted from the out_msstats file. Proteins supported by more peptide
identifications can constitute more confident results.
ProteinGroups Count
Number of protein groups per raw file.
Based on statistics calculated from mzTab, mzIdentML (mzid), or DIA-NN report files.
Peptide ID Count
Number of unique (i.e. not counted twice) peptide sequences including modifications per Raw file.
Based on statistics calculated from mzTab, mzIdentML (mzid), or DIA-NN report files.
Missed Cleavages Per Raw File
Missed Cleavages per raw file.
Under optimal digestion conditions (high enzyme grade etc.), only few missed cleavages (MC) are expected. In
general, increased MC counts also increase the number of peptide signals, thus cluttering the available
space and potentially provoking overlapping peptide signals, biasing peptide quantification.
Thus, low MC counts should be favored. Interestingly, it has been shown recently that
incorporation of peptides with missed cleavages does not negatively influence protein quantification (see
[Chiva, C., Ortega, M., and Sabido, E. Influence of the Digestion Technique, Protease, and Missed
Cleavage Peptides in Protein Quantitation. J. Proteome Res. 2014, 13, 3979-86](https://doi.org/10.1021/pr500294d) ).
However this is true only if all samples show the same degree of digestion. High missed cleavage values
can indicate for example, either a) failed digestion, b) a high (post-digestion) protein contamination, or
c) a sample with high amounts of unspecifically degraded peptides which are not digested by trypsin.
If MC>=1 is high (>20%) you should increase the missed cleavages settings in MaxQuant and compare the number of peptides.
Usually high MC correlates with bad identification rates, since many spectra cannot be matched to the forward database.
In the rare case that 'no enzyme' was specified in MaxQuant, neither scores nor plots are shown.
Modifications Per Raw File
Compute an occurence table of modifications (e.g. Oxidation (M)) for all peptides, including the unmodified.
Post-translational modifications contained within the identified peptide sequence.
MS/MS Identified Per Raw File
MS/MS identification rate per Raw file.
MS/MS identification rate per raw file (quantms data from mzTab and mzML files; MaxQuant data from summary.txt)
Search Engine Scores
Summary of Spectral E-values
This statistic is extracted from idXML files. SpecEvalue: Spectral E-values, the search score of MSGF. The value used for plotting is -lg(SpecEvalue).
Summary of cross-correlation scores
This statistic is extracted from idXML files. xcorr: cross-correlation scores, the search score of Comet. The value used for plotting is xcorr.
Summary of Search Engine PEP
This statistic is extracted from idXML files.
Consensus Across Search Engines
Consensus support is a measure of agreement between search engines.
Every peptide sequence in the analysis has been identified by at least one search run.
The consensus support defines which fraction (between 0 and 1) of the remaining search runs "supported" a peptide
identification that was kept. The meaning of "support" differs slightly between
algorithms: For best, worst, average and rank, each search run supports peptides that it has also identified among its
top considered_hits candidates. So the "consensus support" simply gives the fraction of additional search engines that
have identified a peptide. (For example, if there are three search runs, peptides identified by two of them will have a
"support" of 0.5.) For the similarity-based algorithms PEPMatrix and PEPIons, the "support" for a peptide is the average
similarity of the most-similar peptide from each (other) search run.
Quantification Analysis
Peptides Quantification Table
This plot shows the quantification information of peptides in the final result (mainly the mzTab file).
The quantification information of peptides is obtained from the MSstats input file.
The table shows the quantitative level and distribution of peptides in different study variables,
run and peptiforms. The distribution show all the intensity values in a bar plot above and below
the average intensity for all the fractions, runs and peptiforms.
* BestSearchScore: It is equal to 1 - min(Q.Value) for DIA datasets. Then it is equal to
1 - min(best_search_engine_score[1]), which is from best_search_engine_score[1] column in mzTab
peptide table for DDA datasets.
* Average Intensity: Average intensity of each peptide sequence across all conditions with NA=0 or NA ignored.
* Peptide intensity in each condition (Eg. `CT=Mixture;CN=UPS1;QY=0.1fmol`): Summarize intensity of fractions,
and then mean intensity in technical replicates/biological replicates separately.
Showing 50/50 rows and 7/7 columns.
| PeptideID | Protein Name | Peptide Sequence | Best Search Score | Average Intensity | SP=Saccharomyces cerevisiae;CT=Mixture;QY=10%;CV=in-house | SP=Saccharomyces cerevisiae;CT=Mixture;QY=5%;CV=in-house | SP=Saccharomyces cerevisiae;CT=Mixture;QY=3.3%;CV=in-house |
|---|---|---|---|---|---|---|---|
| 1 | sp|P55011|S12A2_HUMAN | AAAAAAAAAAAAAAAGAGAGAK | 1.0000 | 7.5635 | 7.8547 | 7.1305 | 7.2778 |
| 2 | sp|Q5TF21|SOGA3_HUMAN | AAAAAAAAQMHAK | 0.9999 | 6.8194 | 6.8194 | 0.0000 | 0.0000 |
| 3 | sp|Q9Y4H2|IRS2_HUMAN | AAAAAAAAVPSAGPAGPAPTSAAGR | 1.0000 | 7.5487 | 7.5525 | 0.0000 | 7.5409 |
| 4 | sp|P36578|RL4_HUMAN | AAAAAAALQAK | 1.0000 | 9.0107 | 9.0444 | 8.8642 | 9.0992 |
| 5 | sp|Q6SPF0|SAMD1_HUMAN | AAAAAATAPPSPGPAQPGPR | 1.0000 | 7.5572 | 7.4772 | 7.5710 | 7.6038 |
| 6 | sp|Q8WUQ7|CATIN_HUMAN | AAAAALSQQQSLQER | 1.0000 | 7.2535 | 0.0000 | 0.0000 | 7.2535 |
| 7 | sp|A6NIH7|U119B_HUMAN | AAAAASAAGPGGLVAGK | 0.9999 | 7.6843 | 7.6746 | 7.6609 | 7.7244 |
| 8 | sp|Q9P258|RCC2_HUMAN | AAAAAWEEPSSGNGTAR | 1.0000 | 8.0796 | 7.9541 | 8.1012 | 8.1353 |
| 9 | sp|Q96L91|EP400_HUMAN | AAAAPFQTSQASASAPR | 1.0000 | 7.1562 | 7.2321 | 7.1622 | 7.1302 |
| 10 | sp|P52701|MSH6_HUMAN | AAAAPGASPSPGGDAAWSEAGPGPR | 1.0000 | 7.2481 | 7.2991 | 7.2426 | 7.1980 |
| 11 | sp|P52701|MSH6_HUMAN | AAAAPGASPSPGGDAAWSEAGPGPRPLAR | 1.0000 | 7.5203 | 7.4933 | 7.5347 | 7.5254 |
| 12 | sp|P55036|PSMD4_HUMAN | AAAASAAEAGIATTGTEDSDDALLK | 1.0000 | 7.9610 | 7.8871 | 7.9954 | 7.9677 |
| 13 | sp|A1X283|SPD2B_HUMAN | AAAASVPNADGLK | 1.0000 | 8.0871 | 0.0000 | 0.0000 | 8.0871 |
| 14 | sp|Q8WWH5|TRUB1_HUMAN | AAAAVVAAAAR | 1.0000 | 7.4872 | 7.5504 | 7.3935 | 7.5348 |
| 15 | sp|P09938|RIR2_YEAST | AAADALSDLEIK | 1.0000 | 7.8338 | 8.0377 | 7.7404 | 7.5226 |
| 16 | sp|Q96GQ5|RUSF1_HUMAN | AAADGSLQWEVGGWR | 0.9977 | 7.3983 | 7.3983 | 0.0000 | 0.0000 |
| 17 | sp|O95159|ZFPL1_HUMAN | AAADSDPNLDPLMNPHIR | 1.0000 | 7.4766 | 7.4766 | 0.0000 | 0.0000 |
| 18 | sp|Q6P2E9|EDC4_HUMAN | AAADTLQGPMQAAYR | 1.0000 | 8.2114 | 8.1032 | 0.0000 | 8.2710 |
| 19 | sp|Q9NQP4|PFD4_HUMAN | AAAEDVNVTFEDQQK | 1.0000 | 8.4535 | 8.4058 | 8.4546 | 8.4851 |
| 20 | sp|O15357|SHIP2_HUMAN | AAAEELLAR | 0.9999 | 7.5581 | 0.0000 | 7.6075 | 7.5024 |
| 21 | sp|P30260|CDC27_HUMAN | AAAEGLMSLLR | 1.0000 | 7.2680 | 6.9462 | 7.3082 | 7.3230 |
| 22 | sp|P15180|SYKC_YEAST | AAAEGVANLHLDEATGEMVSK | 1.0000 | 7.8182 | 7.9145 | 7.7394 | 7.5220 |
| 23 | sp|Q9NP50|SHCAF_HUMAN | AAAEKPEEQGPEPLPISTQEW | 1.0000 | 7.0767 | 7.2119 | 0.0000 | 6.8793 |
| 24 | sp|Q01780|EXOSX_HUMAN | AAAEQAISVR | 1.0000 | 7.8330 | 0.0000 | 0.0000 | 7.8330 |
| 25 | sp|P02786|TFR1_HUMAN | AAAEVAGQFVIK | 1.0000 | 7.8392 | 7.8840 | 7.8143 | 7.7924 |
| 26 | sp|P07954|FUMH_HUMAN | AAAEVNQDYGLDPK | 1.0000 | 8.6800 | 8.6560 | 8.6742 | 8.7027 |
| 27 | sp|Q9Y490|TLN1_HUMAN | AAAFEEQENETVVVK | 1.0000 | 8.2954 | 8.2969 | 8.3068 | 8.2885 |
| 28 | sp|O94826|TOM70_HUMAN | AAAFEQLQK | 1.0000 | 8.4147 | 8.3728 | 8.4191 | 8.4394 |
| 29 | sp|Q96S52|PIGS_HUMAN | AAAGAAATHLEVAR | 1.0000 | 8.1765 | 8.1765 | 0.0000 | 0.0000 |
| 30 | sp|Q9H3U1|UN45A_HUMAN | AAAGGLAMLTSMR | 1.0000 | 7.4325 | 0.0000 | 7.4304 | 7.4346 |
| 31 | sp|Q9GZT9|EGLN1_HUMAN | AAAGGQGSAVAAEAEPGK | 1.0000 | 7.5986 | 7.5986 | 0.0000 | 0.0000 |
| 32 | sp|Q9GZT9|EGLN1_HUMAN | AAAGGQGSAVAAEAEPGKEEPPAR | 1.0000 | 7.3369 | 7.3028 | 7.2321 | 7.5142 |
| 33 | sp|P17544|ATF7_HUMAN | AAAGPLDMSLPSTPDIK | 1.0000 | 7.1283 | 6.8848 | 7.1499 | 7.1940 |
| 34 | sp|O14497|ARI1A_HUMAN | AAAGQESEGPAVGPPQPLGK | 1.0000 | 7.8403 | 7.8201 | 7.8202 | 7.8712 |
| 35 | sp|Q9BR61|ACBD6_HUMAN | AAAHLQGLIQVASR | 1.0000 | 7.3271 | 7.2245 | 7.3748 | 7.3664 |
| 36 | sp|P0DMV8|HS71A_HUMAN;sp|P0DMV9|HS71B_HUMAN | AAAIGIDLGTTYSCVGVFQHGK | 1.0000 | 7.1289 | 7.1289 | 0.0000 | 0.0000 |
| 37 | sp|Q8TAE8|G45IP_HUMAN | AAALAAAVAQDPAASGAPSS | 1.0000 | 7.6476 | 7.6741 | 0.0000 | 7.6290 |
| 38 | sp|Q14008|CKAP5_HUMAN | AAALATVNAWAEQTGMK | 1.0000 | 7.6494 | 7.5710 | 7.6614 | 7.6891 |
| 39 | sp|P31948|STIP1_HUMAN | AAALEAMK | 1.0000 | 8.3820 | 8.3476 | 8.4448 | 8.3372 |
| 40 | sp|P31948|STIP1_HUMAN | AAALEFLNR | 1.0000 | 9.0951 | 9.0992 | 9.1237 | 9.0613 |
| 41 | sp|Q68DK7|MSL1_HUMAN | AAALGGPEDEPGAAEAHFLPR | 1.0000 | 7.4992 | 0.0000 | 7.4992 | 0.0000 |
| 42 | sp|Q12955|ANK3_HUMAN | AAALLLQNDNNADVESK | 1.0000 | 6.7923 | 6.7923 | 0.0000 | 0.0000 |
| 43 | sp|Q8NAF0|ZN579_HUMAN | AAALQALQAQAPTSPPPPPPPLK | 0.9996 | 7.0332 | 0.0000 | 0.0000 | 7.0332 |
| 44 | sp|P56945|BCAR1_HUMAN | AAALQYPSPSAAQDMVER | 1.0000 | 7.4761 | 7.4345 | 0.0000 | 7.4956 |
| 45 | sp|Q14498|RBM39_HUMAN | AAAMANNLQK | 1.0000 | 8.0971 | 8.0314 | 8.1297 | 8.1088 |
| 46 | sp|Q9BQ04|RBM4B_HUMAN | AAAMLPTVGEGYGYGPESELSQASAATR | 1.0000 | 6.8052 | 6.8052 | 0.0000 | 0.0000 |
| 47 | sp|Q9BQ04|RBM4B_HUMAN | AAAMLPTVGEGYGYGPESELSQASAATR | 1.0000 | 7.6162 | 7.4976 | 7.7093 | 0.0000 |
| 48 | sp|Q15042|RB3GP_HUMAN | AAAMTPPEEELK | 1.0000 | 8.1021 | 0.0000 | 0.0000 | 8.1021 |
| 49 | sp|Q6UB99|ANR11_HUMAN | AAAPAEGPPGGIQPEAAEPKPTAEAPK | 1.0000 | 7.1285 | 0.0000 | 7.0511 | 7.2512 |
| 50 | sp|P31350|RIR2_HUMAN | AAAPGVEDEPLLR | 1.0000 | 8.4047 | 8.4146 | 8.4046 | 8.3971 |
Protein Quantification Table
This plot shows the quantification information of proteins in the final result (mainly the mzTab file).
The quantification information of proteins is obtained from the msstats input file.
The table shows the quantitative level and distribution of proteins in different study variables and run.
* Peptides_Number: The number of peptides for each protein.
* Average Intensity: Average intensity of each protein across all conditions with NA=0 or NA ignored.
* Protein intensity in each condition (Eg. `CT=Mixture;CN=UPS1;QY=0.1fmol`): Summarize intensity of peptides.
Showing 50/50 rows and 6/6 columns.
| ProteinID | Protein Name | Number of Peptides | Average Intensity | SP=Saccharomyces cerevisiae;CT=Mixture;QY=10%;CV=in-house | SP=Saccharomyces cerevisiae;CT=Mixture;QY=5%;CV=in-house | SP=Saccharomyces cerevisiae;CT=Mixture;QY=3.3%;CV=in-house |
|---|---|---|---|---|---|---|
| 1 | sp|A0A024RBG1|NUD4B_HUMAN;sp|Q9NZJ9|NUDT4_HUMAN | 1 | 7.6182 | 7.7904 | 7.6751 | 7.4865 |
| 2 | sp|A0A0A6YYC5|TVA14_HUMAN | 1 | 7.8080 | 0.0000 | 7.8080 | 0.0000 |
| 3 | sp|A0A0B4J2D5|GAL3B_HUMAN;sp|P0DPI2|GAL3A_HUMAN | 7 | 9.0060 | 8.9366 | 8.9477 | 9.0210 |
| 4 | sp|A0AVF1|IFT56_HUMAN | 3 | 7.6152 | 7.4290 | 7.3572 | 7.4632 |
| 5 | sp|A0AVT1|UBA6_HUMAN | 19 | 9.1927 | 9.0894 | 9.1329 | 9.2011 |
| 6 | sp|A0FGR8|ESYT2_HUMAN | 10 | 8.6129 | 8.6282 | 8.6154 | 8.4218 |
| 7 | sp|A0JLT2|MED19_HUMAN | 1 | 7.0329 | 7.1629 | 7.0350 | 6.9910 |
| 8 | sp|A0JNW5|UH1BL_HUMAN | 4 | 7.6573 | 7.0527 | 6.9881 | 7.6277 |
| 9 | sp|A0MZ66|SHOT1_HUMAN | 11 | 8.5320 | 8.1621 | 8.5007 | 8.3048 |
| 10 | sp|A0PJW6|TM223_HUMAN | 1 | 7.2444 | 7.1459 | 7.2162 | 7.3152 |
| 11 | sp|A0PK00|T120B_HUMAN | 2 | 7.3972 | 0.3010 | 7.1447 | 7.4736 |
| 12 | sp|A1L020|MEX3A_HUMAN | 8 | 8.5322 | 8.4172 | 8.4686 | 8.3708 |
| 13 | sp|A1L0T0|HACL2_HUMAN | 10 | 8.4628 | 8.4028 | 8.2699 | 8.3568 |
| 14 | sp|A1L390|PKHG3_HUMAN | 3 | 7.4518 | 7.0501 | 6.9547 | 7.3954 |
| 15 | sp|A1X283|SPD2B_HUMAN | 24 | 9.1792 | 9.0516 | 9.1259 | 9.1847 |
| 16 | sp|A2RRP1|NBAS_HUMAN | 19 | 8.6161 | 8.5332 | 8.4991 | 8.5837 |
| 17 | sp|A2RUC4|TYW5_HUMAN | 4 | 7.5948 | 7.3995 | 6.9391 | 7.3579 |
| 18 | sp|A3KMH1|VWA8_HUMAN | 24 | 8.7303 | 8.6333 | 8.4186 | 8.5408 |
| 19 | sp|A3KN83|SBNO1_HUMAN | 9 | 8.2706 | 8.1618 | 7.9632 | 7.8503 |
| 20 | sp|A4D161|F221A_HUMAN | 2 | 8.3032 | 7.0180 | 8.3041 | 7.0936 |
| 21 | sp|A4D1E9|GTPBA_HUMAN | 5 | 7.8848 | 7.2447 | 7.6046 | 7.7357 |
| 22 | sp|A4D1P6|WDR91_HUMAN | 4 | 8.2410 | 6.9871 | 7.3815 | 8.1742 |
| 23 | sp|A4D2B8|PM2P1_HUMAN | 1 | 7.9174 | 0.0000 | 0.0000 | 7.9174 |
| 24 | sp|A5D8V6|VP37C_HUMAN | 1 | 7.6780 | 7.6054 | 7.7104 | 7.7100 |
| 25 | sp|A5YKK6|CNOT1_HUMAN | 41 | 9.2554 | 9.1394 | 9.0876 | 9.2288 |
| 26 | sp|A5YM72|CRNS1_HUMAN | 1 | 7.5357 | 7.5357 | 0.0000 | 0.0000 |
| 27 | sp|A5Z2X5|YP010_YEAST | 1 | 6.8123 | 6.8123 | 0.0000 | 0.0000 |
| 28 | sp|A6NCE7|MP3B2_HUMAN;sp|Q9GZQ8|MLP3B_HUMAN | 1 | 8.4373 | 8.4216 | 8.4021 | 8.4650 |
| 29 | sp|A6NDB9|PALM3_HUMAN | 4 | 7.6389 | 7.0067 | 7.4330 | 7.5512 |
| 30 | sp|A6NDG6|PGP_HUMAN | 4 | 8.3821 | 8.3570 | 8.3769 | 8.4088 |
| 31 | sp|A6NDU8|CE051_HUMAN | 2 | 7.8688 | 7.7968 | 7.7529 | 7.8533 |
| 32 | sp|A6NFI3|ZN316_HUMAN | 3 | 8.2524 | 7.5023 | 7.4077 | 8.0845 |
| 33 | sp|A6NGN9|IGLO5_HUMAN | 5 | 8.1099 | 7.8933 | 8.0293 | 8.1413 |
| 34 | sp|A6NHL2|TBAL3_HUMAN | 2 | 8.1946 | 8.0283 | 8.1988 | 8.1949 |
| 35 | sp|A6NHQ2|FBLL1_HUMAN | 5 | 8.9518 | 8.9035 | 8.9699 | 8.9462 |
| 36 | sp|A6NHR9|SMHD1_HUMAN | 46 | 9.7252 | 9.5768 | 9.5881 | 9.7305 |
| 37 | sp|A6NIH7|U119B_HUMAN | 2 | 7.7692 | 7.6746 | 7.7509 | 7.8008 |
| 38 | sp|A6NJ78|MET15_HUMAN | 3 | 7.7809 | 7.2598 | 0.4771 | 7.8244 |
| 39 | sp|A6NKD9|CC85C_HUMAN | 4 | 7.7193 | 7.3300 | 6.9448 | 7.6628 |
| 40 | sp|A6NKG5|RTL1_HUMAN | 41 | 10.0303 | 9.9912 | 10.0242 | 10.0413 |
| 41 | sp|A6NNE9|MARHB_HUMAN | 1 | 6.7344 | 0.0000 | 0.0000 | 6.7344 |
| 42 | sp|A6ZKI3|RTL8C_HUMAN | 2 | 7.1764 | 0.3010 | 6.7994 | 7.1581 |
| 43 | sp|A7E2V4|ZSWM8_HUMAN | 7 | 8.3233 | 7.4292 | 7.7053 | 8.3208 |
| 44 | sp|A7KAX9|RHG32_HUMAN | 2 | 7.8542 | 0.3010 | 0.3010 | 7.8542 |
| 45 | sp|A8CG34|P121C_HUMAN | 2 | 7.7171 | 7.6553 | 7.6905 | 7.7313 |
| 46 | sp|A8MT69|CENPX_HUMAN | 1 | 7.7040 | 7.6315 | 7.7263 | 7.6915 |
| 47 | sp|A8MTJ3|GNAT3_HUMAN | 1 | 7.3227 | 0.0000 | 7.3227 | 0.0000 |
| 48 | sp|A8MUU1|FB5L3_HUMAN | 1 | 8.3119 | 8.3119 | 0.0000 | 0.0000 |
| 49 | sp|A8MVJ9|HPF1L_HUMAN | 1 | 7.2282 | 0.0000 | 0.0000 | 7.2282 |
| 50 | sp|A8MVW0|F1712_HUMAN | 3 | 7.6607 | 7.5248 | 7.1330 | 7.6752 |
Peptide Intensity Distribution
Peptide intensity per file from mzTab.
Calculate the average of peptide_abundance_study_variable[1-n] values for each peptide from the
peptide table in the mzTab file, and then apply a log2 transformation.
Created with MultiQC
MS1 Analysis
Total Ion Chromatograms
MS1 quality control information extracted from the spectrum files.
This plot displays Total Ion Chromatograms (TICs) derived from MS1 scans across all analyzed samples.
The x-axis represents retention time, and the y-axis shows the total ion intensity at each time point.
Each colored trace corresponds to a different sample. The TIC provides a global view of the ion signal
throughout the LC-MS/MS run, reflecting when compounds elute from the chromatography column.
Key aspects to assess include:
* Overall intensity pattern: A consistent baseline and similar peak profiles across samples indicate good reproducibility.
* Major peak alignment: Prominent peaks appearing at similar retention times suggest stable chromatographic performance.
* Signal-to-noise ratio: High peaks relative to baseline noise reflect better sensitivity.
* Chromatographic resolution: Sharp, well-separated peaks indicate effective separation.
* Signal drift: A gradual decline in signal intensity across the run may point to source contamination or chromatography issues.
Deviations such as shifted retention times, missing peaks, or inconsistent intensities may signal problems
in sample preparation, LC conditions, or mass spectrometer performance that require further investigation.
Created with MultiQC
MS1 Base Peak Chromatograms
MS1 base peak chromatograms extracted from the spectrum files.
The Base Peak Chromatogram (BPC) displays the intensity of the most abundant ion at each retention
time point across your LC-MS run. Unlike the Total Ion Chromatogram (TIC) which shows the summed
intensity of all ions, the BPC highlights the strongest signals, providing better visualization
of compounds with high abundance while reducing baseline noise. This makes it particularly useful
for identifying major components in complex samples, monitoring dominant species, and providing
clearer peak visualization when signal-to-noise ratio is a concern. Comparing BPC patterns across
samples allows you to evaluate consistency in the detection of high-abundance compounds
and can reveal significant variations in sample composition or instrument performance.
Created with MultiQC
MS1 Peaks
MS1 Peaks from the spectrum files
This plot shows the number of peaks detected in MS1 scans over the course of each sample run.
The x-axis represents retention time (in minutes), while the y-axis displays the number of
distinct ion signals (peaks) identified in each MS1 scan. The MS1 peak count reflects spectral
complexity and provides insight into instrument performance during the LC-MS analysis.
Key aspects to consider include:
* Overall pattern: Peak counts typically increase during the elution of complex mixtures and
decrease during column washing or re-equilibration phases.
* Peak density: Higher counts suggest more complex spectra, potentially indicating a greater
number of compounds present at that time point."
* Peak Consistency across samples: Similar profiles among replicates or related samples
indicate good analytical reproducibility.
* Sudden drops: Abrupt decreases in peak count may point to transient ionization issues,
spray instability, or chromatographic disruptions.
* Baseline values: The minimum peak count observed reflects the level of background noise
or instrument sensitivity in the absence of eluting compounds.
Monitoring MS1 peak counts complements total ion chromatogram (TIC) and base peak chromatogram
(BPC) data, offering an additional layer of quality control related to signal complexity,
instrument stability, and sample composition.
Created with MultiQC
General stats for MS1 information
General stats for MS1 information extracted from the spectrum files.
This table presents general statistics for MS1 information extracted from mass spectrometry data files."
It displays MS runs with their acquisition dates and times.
For each file, the table shows two key metrics: TotalCurrent (the sum of all MS1 ion intensities throughout the run) and ScanCurrent (the sum of MS2 ion intensities).
These values provide a quick overview of the total ion signals detected during both survey scans (MS1) and fragmentation scans (MS2),
allowing for comparison of overall signal intensity across samples. Consistent TotalCurrent and ScanCurrent values across similar samples typically
indicate good reproducibility in the mass spectrometry analysis, while significant variations may suggest issues with sample preparation,
instrument performance, or ionization efficiency. The blue shading helps visualize the relative intensity differences between samples.
Showing 11/11 rows and 3/3 columns.
| File | Acquisition Date Time | log10(Total Current) | log10(Scan Current) |
|---|---|---|---|
| a05191 | 2017-04-19 12:31:30 | 13.2781 | 11.3916 |
| a05192 | 2017-04-19 16:00:49 | 13.3983 | 11.4832 |
| a05194 | 2017-04-19 23:00:02 | 13.4088 | 11.5156 |
| a05195 | 2017-04-20 02:29:46 | 13.3459 | 11.4725 |
| a05196 | 2017-04-20 05:59:32 | 13.3672 | 11.4841 |
| a05197 | 2017-04-20 09:29:26 | 13.3514 | 11.4879 |
| a05198 | 2017-04-20 12:59:18 | 13.3825 | 11.4890 |
| a05199 | 2017-04-20 16:29:14 | 13.4123 | 11.5023 |
| a05200 | 2017-04-20 19:59:09 | 13.3301 | 11.4724 |
| a05201 | 2017-04-20 23:29:04 | 13.4156 | 11.5317 |
| a05202 | 2017-04-21 02:58:59 | 13.3374 | 11.4784 |
MS2 and Spectral Stats
Number of Peaks per MS/MS spectrum
This chart represents a histogram containing the number of peaks per MS/MS spectrum
in a given experiment.
This chart assumes centroid data. Too few peaks can identify poor fragmentation
or a detector fault, as opposed to a large number of peaks representing very noisy spectra.
This chart is extensively dependent on the pre-processing steps performed to the spectra
(centroiding, deconvolution, peak picking approach, etc).
Peak Intensity Distribution
This is a histogram representing the ion intensity vs.
the frequency for all MS2 spectra in a whole given experiment.
It is possible to filter the information for all, identified and unidentified spectra.
This plot can give a general estimation of the noise level of the spectra.
Generally, one should expect to have a high number of low intensity noise peaks with a low number
of high intensity signal peaks.
A disproportionate number of high signal peaks may indicate heavy spectrum pre-filtering or
potential experimental problems. In the case of data reuse this plot can be useful in
identifying the requirement for pre-processing of the spectra prior to any downstream analysis.
The quality of the identifications is not linked to this data as most search engines perform internal
spectrum pre-processing before matching the spectra. Thus, the spectra reported are not
necessarily pre-processed since the search engine may have applied the pre-processing step
internally. This pre-processing is not necessarily reported in the experimental metadata.
Pipeline Spectrum Tracking
This plot shows the tracking of the number of spectra along the quantms pipeline
This table shows the changes in the number of spectra corresponding to each input file
during the pipeline operation. And the number of peptides finally identified and quantified is obtained from
the PSM table in the mzTab file. You can also remove decoys with the `remove_decoy` parameter.:
* MS1_Num: The number of MS1 spectra extracted from mzMLs
* MS2_Num: The number of MS2 spectra extracted from mzMLs
* MSGF: The Number of spectra identified by MSGF search engine
* Comet: The Number of spectra identified by Comet search engine
* Sage: The Number of spectra identified by Sage search engine
* PSMs from quant. peptides: extracted from PSM table in mzTab file
* Peptides quantified: extracted from PSM table in mzTab file
Showing 11/11 rows and 6/6 columns.
| Spectra File | #MS1 Spectra | #MS2 Spectra | MSGF | Comet | #PSMs from quant. peptides | #Peptides quantified |
|---|---|---|---|---|---|---|
| a05191 | 13776 | 71049 | 57924 | 56965 | 46612 | 32650 |
| a05192 | 13613 | 73549 | 60638 | 59470 | 49573 | 35498 |
| a05194 | 13450 | 74198 | 60932 | 59956 | 49928 | 36652 |
| a05195 | 13296 | 72696 | 59554 | 58516 | 48629 | 35805 |
| a05196 | 13420 | 73023 | 59967 | 58738 | 48929 | 36063 |
| a05197 | 13258 | 73458 | 60032 | 59227 | 49038 | 36150 |
| a05198 | 13423 | 73438 | 60113 | 59008 | 48700 | 35939 |
| a05199 | 13443 | 73666 | 60381 | 59432 | 49130 | 35596 |
| a05200 | 12979 | 73290 | 60109 | 59215 | 49118 | 36025 |
| a05201 | 13360 | 74857 | 61266 | 60081 | 49555 | 36140 |
| a05202 | 13243 | 73524 | 60284 | 59242 | 48636 | 35837 |
Distribution of Precursor Charges
This is a bar chart representing the distribution of the precursor ion charges for a given whole experiment.
This information can be used to identify potential ionization problems
including many 1+ charges from an ESI ionization source or an unexpected
distribution of charges. MALDI experiments are expected to contain almost exclusively 1+
charged ions. An unexpected charge distribution may furthermore be caused by specific search
engine parameter settings such as limiting the search to specific ion charges.
Charge-state of Per File
The distribution of precursor ion charge states (based on mzTab data).
The distribution of precursor ion charge states (based on mzTab data).
MS/MS Counts Per 3D-peak
An oversampled 3D-peak is defined as a peak whose peptide ion
(same sequence and same charge state) was identified by at least two distinct MS2 spectra
in the same Raw file.
For high complexity samples, oversampling of individual 3D-peaks automatically leads to
undersampling or even omission of other 3D-peaks, reducing the number of identified peptides.
Oversampling occurs in low-complexity samples or long LC gradients, as well as undersized dynamic
exclusion windows for data independent acquisitions.
Time and Mass Error Trends
IDs over RT
Distribution of retention time, derived from the mzTab.
The uncalibrated retention time in minutes in the elution profile of the precursor ion.
Created with MultiQC
Delta Mass [Da]
This chart represents the distribution of the relative frequency of experimental
precursor ion mass (m/z) - theoretical precursor ion mass (m/z).
Mass deltas close to zero reflect more accurate identifications and also
that the reporting of the amino acid modifications and charges have been done accurately.
This plot can highlight systematic bias if not centered on zero.
Other distributions can reflect modifications not being reported properly.
Also it is easy to see the different between the target and the decoys identifications.
Created with MultiQC
Delta Mass [ppm]
Delta Mass [ppm] calculated from mzTab.
Delta Mass [ppm] calculated from mzTab: ((𝑒𝑥𝑝𝑒𝑟𝑖𝑚𝑒𝑛𝑡𝑎𝑙 𝑚/𝑧 - 𝑡ℎ𝑒𝑜𝑟𝑒𝑡𝑖𝑐𝑎𝑙 𝑚/𝑧) / (𝑡ℎ𝑒𝑜𝑟𝑒𝑡𝑖𝑐𝑎𝑙 𝑚/𝑧)) × 10^6.
Created with MultiQC
Software Versions
Software Versions lists versions of software tools extracted from file contents.
| Group | Software | Version |
|---|---|---|
| CONSENSUSID | ConsensusID | 3.3.0-pre-exported-20250122 |
| DECOYDATABASE | DecoyDatabase | 3.3.0-pre-exported-20250122 |
| FDRCONSENSUSID | FalseDiscoveryRate | 3.3.0-pre-exported-20250122 |
| IDFILTER | IDFilter | 3.3.0-pre-exported-20250122 |
| MS2RESCORE | MS2Rescore | 3.1.4 |
| deeplc | 3.1.8 | |
| ms2pip | 4.1.0 | |
| quantms-rescoring | 0.0.5 | |
| MSGFDBINDEXING | msgf_plus | MS-GF+ Release (v2024.03.26) (26 March 2024) |
| MZMLSTATISTICS | quantms-utils | 0.0.20 |
| PERCOLATOR | PercolatorAdapter | 3.3.0-pre-exported-20250122 |
| percolator | 3.05.0, Build Date Aug 31 2020 19:03:04 | |
| PROTEOMICSLFQ | ProteomicsLFQ | 3.3.0-pre-exported-20250122 |
| SAMPLESHEET_CHECK | quantms-utils | 0.0.20 |
| SDRFPARSING | sdrf-pipelines | 0.0.31 |
| SEARCHENGINECOMET | Comet | 2023.01 rev. 2 |
| CometAdapter | 3.3.0-pre-exported-20250122 | |
| SEARCHENGINEMSGF | MSGFPlusAdapter | 3.3.0-pre-exported-20250122 |
| msgf_plus | MS-GF+ Release (v2023.01.12) (12 January 2023) | |
| THERMORAWFILEPARSER | ThermoRawFileParser | 1.3.4 |
| Workflow | Nextflow | 24.10.5 |
| bigbio/quantms | v1.4.0dev |
bigbio/quantms Workflow Summary
- this information is collected when the pipeline is started.URL: https://github.com/bigbio/quantms
Input/output options
- export_decoy_psm
- true
- input
- /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/mcp/PXD007683/PXD007683-LFQ.sdrf.tsv
- local_input_type
- raw
- outdir
- ./PXD007683_msgf_comet_ms2rescore_quant
- root_folder
- /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/mcp/PXD007683
SDRF validation
- skip_factor_validation
- true
- use_ols_cache_only
- true
- validate_ontologies
- true
Protein database
- add_decoys
- true
- database
- /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/mcp/PXD007683/uniprot-UP000005640_UP000002311_reviewed.fasta
Database search
- search_engines
- msgf,comet
Modification localization
- luciphor_debug
- 0
PSM re-scoring (general)
- ms2pip_model
- HCD
- ms2pip_model_dir
- /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/model_file/
- ms2rescore
- true
- run_fdr_cutoff
- 0.10
PSM re-scoring (Percolator)
- description_correct_features
- 0
Consensus ID
- consensusid_considered_top_hits
- 0
- min_consensus_support
- 0
Isobaric analyzer
- quant_activation_method
- HCD
Protein Quantification (LFQ)
- feature_with_id_min_score
- 0.10
Statistical post-processing
- contrasts
- pairwise
- skip_post_msstats
- true
Quality control
- enable_pmultiqc
- true
- pmultiqc_idxml_skip
- true
Generic options
- trace_report_suffix
- 2025-03-30_11-14-03
Core Nextflow options
- configFiles
- /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/mcp/gitrepo/new_quantms/quantms/nextflow.config, /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/mcp/k8s_next.config
- containerEngine
- docker
- launchDir
- /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/mcp/gitrepo/new_quantms/quantms
- profile
- docker
- projectDir
- /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/mcp/gitrepo/new_quantms/quantms
- runName
- mad_hodgkin
- userName
- daicx
- workDir
- /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/mcp/gitrepo/new_quantms/quantms/work
bigbio/quantms Methods Description
Suggested text and references to use when describing pipeline usage within the methods section of a publication.URL: https://github.com/bigbio/quantms
Methods
Data was processed using bigbio/quantms v1.4.0dev (doi: 10.5281/zenodo.7754148) of the nf-core collection of workflows (Ewels et al., 2020), utilising reproducible software environments from the Bioconda (Grüning et al., 2018) and Biocontainers (da Veiga Leprevost et al., 2017) projects.
The pipeline was executed with Nextflow v24.10.5 (Di Tommaso et al., 2017) with the following command:
nextflow run main.nf --input /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/mcp/PXD007683/PXD007683-LFQ.sdrf.tsv --database /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/mcp/PXD007683/uniprot-UP000005640_UP000002311_reviewed.fasta --add_decoys true --search_engines msgf,comet -resume PXD007683 -c /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/mcp/k8s_next.config -profile docker --ms2rescore true --ms2pip_model_dir /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/model_file/ --root_folder /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/mcp/PXD007683 --local_input_type raw --ms2pip_model HCD --outdir ./PXD007683_msgf_comet_ms2rescore_quant --skip_post_msstats trueReferences
- Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., & Notredame, C. (2017). Nextflow enables reproducible computational workflows. Nature Biotechnology, 35(4), 316-319. doi: 10.1038/nbt.3820
- Ewels, P. A., Peltzer, A., Fillinger, S., Patel, H., Alneberg, J., Wilm, A., Garcia, M. U., Di Tommaso, P., & Nahnsen, S. (2020). The nf-core framework for community-curated bioinformatics pipelines. Nature Biotechnology, 38(3), 276-278. doi: 10.1038/s41587-020-0439-x
- Grüning, B., Dale, R., Sjödin, A., Chapman, B. A., Rowe, J., Tomkins-Tinch, C. H., Valieris, R., Köster, J., & Bioconda Team. (2018). Bioconda: sustainable and comprehensive software distribution for the life sciences. Nature Methods, 15(7), 475–476. doi: 10.1038/s41592-018-0046-7
- da Veiga Leprevost, F., Grüning, B. A., Alves Aflitos, S., Röst, H. L., Uszkoreit, J., Barsnes, H., Vaudel, M., Moreno, P., Gatto, L., Weber, J., Bai, M., Jimenez, R. C., Sachsenberg, T., Pfeuffer, J., Vera Alvarez, R., Griss, J., Nesvizhskii, A. I., & Perez-Riverol, Y. (2017). BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics (Oxford, England), 33(16), 2580–2582. doi: 10.1093/bioinformatics/btx192
Notes:
- The command above does not include parameters contained in any configs or profiles that may have been used. Ensure the config file is also uploaded with your publication!
- You should also cite all software used within this run. Check the "Software Versions" of this report to get version information.
